Towards Proactive Personalization through Profile Customization for Individual Users in Dialogues

Fuente: arXiv

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Bibliographic Details
Main Authors:	Zhang, Xiaotian, Wang, Yuan, Chen, Ruizhe, Wang, Zeya, Hou, Runchen, Liu, Zuozhu
Format:	Preprint
Published:	2025
Subjects:	Computation and Language
Online Access:	Acceder al recurso
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_version_	1866908718071808000
author	Zhang, Xiaotian Wang, Yuan Chen, Ruizhe Wang, Zeya Hou, Runchen Liu, Zuozhu
author_facet	Zhang, Xiaotian Wang, Yuan Chen, Ruizhe Wang, Zeya Hou, Runchen Liu, Zuozhu
contents	The deployment of Large Language Models (LLMs) in interactive systems necessitates a deep alignment with the nuanced and dynamic preferences of individual users. Current alignment techniques predominantly address universal human values or static, single-turn preferences, thereby failing to address the critical needs of long-term personalization and the initial user cold-start problem. To bridge this gap, we propose PersonalAgent, a novel user-centric lifelong agent designed to continuously infer and adapt to user preferences. PersonalAgent constructs and dynamically refines a unified user profile by decomposing dialogues into single-turn interactions, framing preference inference as a sequential decision-making task. Experiments show that PersonalAgent achieves superior performance over strong prompt-based and policy optimization baselines, not only in idealized but also in noisy conversational contexts, while preserving cross-session preference consistency. Furthermore, human evaluation confirms that PersonalAgent excels at capturing user preferences naturally and coherently. Our findings underscore the importance of lifelong personalization for developing more inclusive and adaptive conversational agents. Our code is available here.
format	Preprint
id	arxiv_https___arxiv_org_abs_2512_15302
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	Towards Proactive Personalization through Profile Customization for Individual Users in Dialogues Zhang, Xiaotian Wang, Yuan Chen, Ruizhe Wang, Zeya Hou, Runchen Liu, Zuozhu Computation and Language The deployment of Large Language Models (LLMs) in interactive systems necessitates a deep alignment with the nuanced and dynamic preferences of individual users. Current alignment techniques predominantly address universal human values or static, single-turn preferences, thereby failing to address the critical needs of long-term personalization and the initial user cold-start problem. To bridge this gap, we propose PersonalAgent, a novel user-centric lifelong agent designed to continuously infer and adapt to user preferences. PersonalAgent constructs and dynamically refines a unified user profile by decomposing dialogues into single-turn interactions, framing preference inference as a sequential decision-making task. Experiments show that PersonalAgent achieves superior performance over strong prompt-based and policy optimization baselines, not only in idealized but also in noisy conversational contexts, while preserving cross-session preference consistency. Furthermore, human evaluation confirms that PersonalAgent excels at capturing user preferences naturally and coherently. Our findings underscore the importance of lifelong personalization for developing more inclusive and adaptive conversational agents. Our code is available here.
title	Towards Proactive Personalization through Profile Customization for Individual Users in Dialogues
topic	Computation and Language
url	https://arxiv.org/abs/2512.15302